Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery

The key technology of a battery management system is to online estimate the battery states accurately and robustly. For lithium iron phosphate battery, the relationship between state of charge and open circuit voltage has a plateau region which limits the estimation accuracy of voltage-based algorit...

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Vydané v:	Energy (Oxford) Ročník 112; s. 469 - 480
Hlavní autori:	Deng, Zhongwei, Yang, Lin, Cai, Yishan, Deng, Hao, Sun, Liu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.10.2016
Predmet:	Available capacity prediction batteries Battery management system Dual adaptive extended Kalman filter hysteresis iron phosphates least squares Least squares support vector machine lithium Online parameter identification prediction State of charge estimation support vector machines temperature Battery management system State of charge estimation Available capacity prediction Least squares support vector machine Online parameter identification Dual adaptive extended Kalman filter
ISSN:	0360-5442
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